diff --git a/mindspore/python/mindspore/scipy/utils.py b/mindspore/python/mindspore/scipy/utils.py
index f6b5117ba8b45e25ccab3983798e253451fb389e..3f90b3fcd3a63b877726d56a9b07339781e30ecf 100644
--- a/mindspore/python/mindspore/scipy/utils.py
+++ b/mindspore/python/mindspore/scipy/utils.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """internal utility functions"""
-from .. import nn, ops
+from .. import ops
 from .. import numpy as mnp
 from ..numpy import where, zeros_like, dot, greater
 from ..ops import functional as F
@@ -68,30 +68,21 @@ def _eps(x):
     return _eps_net(x[(0,) * x.ndim])
 
 
-class _SafeNormalize(nn.Cell):
+def _safe_normalize(x, threshold=None):
     """Normalize method that cast very small results to zero."""
-
-    def __init__(self):
-        """Initialize LineSearch."""
-        super(_SafeNormalize, self).__init__()
-
-    def construct(self, x, threshold=None):
-        x_sum2 = F.reduce_sum(F.pows(x, 2.0))
-        norm = F.pows(x_sum2, 1. / 2.0)
-        if threshold is None:
-            if x.dtype in mstype.float_type:
-                # pick the first element of x to get the eps
-                threshold = _eps(x)
-            else:
-                threshold = 0
-        use_norm = greater(norm, threshold)
-        x_norm = x / norm
-        normalized_x = where(use_norm, x_norm, zeros_like(x))
-        norm = where(use_norm, norm, zeros_like(norm))
-        return normalized_x, norm
-
-
-_safe_normalize = _SafeNormalize()
+    x_sum2 = F.reduce_sum(F.pows(x, 2.0))
+    norm = F.pows(x_sum2, 1. / 2.0)
+    if threshold is None:
+        if x.dtype in (mstype.float32, mstype.float64):
+            # pick the first element of x to get the eps
+            threshold = _eps(x)
+        else:
+            threshold = 0
+    use_norm = greater(norm, threshold)
+    x_norm = x / norm
+    normalized_x = where(use_norm, x_norm, zeros_like(x))
+    norm = where(use_norm, norm, zeros_like(norm))
+    return normalized_x, norm
 
 
 def _normalize_matvec(f):